Systems and methods for detecting mobile device movement within a vehicle using accelerometer data

ABSTRACT

One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis ofaccelerometer data obtained from a mobile device within an interior of avehicle. In particular, various aspects of the disclosure relate toreceiving and transmitting accelerometer data, and analyzing the data todetect movement of the mobile device within the interior of the vehicle.

BACKGROUND

Insurance providers value the safety of drivers and the general public.Detecting likely movement of a mobile device within a vehicle andproviding feedback to the drivers reduces distracted driving andpromotes safety. Although techniques exist to generally capture datafrom sensors on smartphones and in vehicles, they might not provideaccurate and power-efficient methods of detecting movement of a mobiledevice. Further, these techniques may not calculate a risk score basedon the movement of a mobile device, or provide feedback on improving therisk score.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Advantageous solutions to the problems presented above, and other issueswhich will be apparent upon the reading of the present disclosure, maybe to collect acceleration data from an accelerometer associated with amobile device, where the mobile device is located within a vehicle. Insome examples, the acceleration data may be collected at a first timeand at a second time. Accordingly, the acceleration data may beprocessed to remove a gravity component and to generate a processedacceleration data at the first time and at the second time. Theprocessed acceleration data may be used to determine that a mobiledevice movement event has occurred at the second time. Sensor data fromother sensors associated with the mobile device, such as a GPS receiverand/or a gyroscope, may also be collected. The sensor data from theother sensors may be aggregated over a window of time starting at apredetermined duration before the second time and ending atpredetermined duration after the second time. The aggregated sensor datamay be used to calculate a risk score and to generate a notificationwith feedback to the mobile device.

In accordance with further aspects of the present disclosure, a methoddisclosed herein may include collecting acceleration data from anaccelerometer associated with a mobile device, where the mobile deviceis located within a vehicle. In some examples, the acceleration data maybe collected at a first time and at a second time. Accordingly, theacceleration data may be processed to remove a gravity component and togenerate a processed acceleration data at the first time and at thesecond time. The processed acceleration data may be used to determinethat a mobile device movement event has occurred at the second time.Sensor data from other sensors associated with the mobile device, suchas a GPS receiver and/or a gyroscope, may also be collected. The sensordata from the other sensors may be aggregated over a window of timestarting at a predetermined duration before the second time and endingat predetermined duration after the second time. The aggregated sensordata may be used to calculate a risk score and to generate anotification with feedback to the mobile device.

In accordance with further aspects of the present disclosure, acomputer-assisted method of detecting mobile device movement eventsdisclosed herein may include collecting acceleration data from anaccelerometer associated with a mobile device, where the mobile deviceis located within a vehicle. In some examples, the acceleration data maybe collected at a first time and at a second time. Accordingly, theacceleration data may be processed to remove a gravity component and togenerate a processed acceleration data at the first time and at thesecond time. The processed acceleration data may be used to determinethat a mobile device movement event has occurred at the second time.Sensor data from other sensors associated with the mobile device, suchas a GPS receiver and/or a gyroscope, may also be collected. The sensordata from the other sensors may be aggregated over a window of timestarting at a predetermined duration before the second time and endingat predetermined duration after the second time. The aggregated sensordata may be used to calculate a risk score and to generate anotification with feedback to the mobile device.

Other features and advantages of the disclosure will be apparent fromthe additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a block diagram illustrating various components and devicesassociated with an example distracted driving analysis system, accordingto one or more aspects of the disclosure.

FIG. 3 is a block diagram of an example of an implementation of a mobiledevice movement detection system, according to one or more aspects ofthe disclosure.

FIG. 4 is a flowchart of example method steps for receiving andprocessing sensor data from a mobile device, detecting a mobile devicemovement event, aggregating the sensor data from the mobile device,calculating a risk score based on the aggregated data, and providingfeedback, according to one or more aspects of the disclosure.

FIG. 5 is a flowchart of example method steps for detecting a mobiledevice movement event, according to one or more aspects of thedisclosure.

FIG. 6 illustrates a consecutive window approach to detecting mobiledevice movement events, according to one or more aspects of thedisclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a specially-programmed computer system, or a computerprogram product. Accordingly, those aspects may take the form of anentirely hardware embodiment, an entirely software embodiment or anembodiment combining software and hardware aspects. Furthermore, suchaspects may take the form of a computer program product stored by one ormore computer-readable storage media having computer-readable programcode, or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof. In addition, various signals representing data orevents as described herein may be transferred between a source and adestination in the form of electromagnetic waves traveling throughsignal-conducting media such as metal wires, optical fibers, and/orwireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a mobile device movement detectionsystem 101 in a distracted driving analysis system 100 that may be usedaccording to one or more illustrative embodiments of the disclosure. Themobile device movement detection system 101 may have a processor 103 forcontrolling overall operation of the mobile device movement detectionsystem 101 and its associated components, including RAM 105, ROM 107,input/output module 109, and memory 115. The mobile device movementdetection system 101, along with one or more additional devices (e.g.,terminals 141, 151) may correspond to one or more special-purposecomputing devices, such as distracted driving analysis computing devicesor systems, including mobile computing devices (e.g., smartphones, smartterminals, tablets, and the like) and vehicular-based computing devices,configured as described herein for collecting and analyzing sensor datafrom mobile devices associated with vehicles, detecting mobile devicemovement events, determining a risk score, and providing feedbackregarding the risk score.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the mobile device movementdetection system 101 may provide input, and may also include one or moreof a speaker for providing audio output and a video display device forproviding textual, audiovisual and/or graphical output. Software may bestored within memory 115 and/or storage to provide instructions toprocessor 103 for enabling the mobile device movement detection system101 to perform various functions. For example, memory 115 may storesoftware used by the mobile device movement detection system 101, suchas an operating system 117, application programs 119, and an associatedinternal database 121. Processor 103 and its associated components mayallow the mobile device movement detection system 101 to execute aseries of computer-readable instructions to collect and analyze sensordata, detect mobile device movement events, determine risk scores, andprovide feedback regarding risk scores.

The mobile device movement detection system 101 may operate in anetworked environment supporting connections to one or more remotecomputers, such as terminals/devices 141 and 151. The mobile devicemovement detection system 101, and related terminals/devices 141 and151, may be in signal communication with special-purpose devicesinstalled in vehicles, mobile devices that may travel within vehicles,or devices outside of vehicles that are configured to receive andprocess sensor data. Thus, the mobile device movement detection system101 and terminals/devices 141 and 151 may each include personalcomputers (e.g., laptop, desktop, or tablet computers), servers (e.g.,web servers, database servers), vehicle-based devices (e.g., on-boardvehicle computers, short-range vehicle communication systems, telematicsdevices), or mobile communication devices (e.g., mobile phones, portablecomputing devices, and the like), and may include some or all of theelements described above with respect to the mobile device movementdetection system 101.

The network connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, and a wirelesstelecommunications network 133, but may also include other networks.When used in a LAN networking environment, the mobile device movementdetection system 101 may be connected to the LAN 125 through a networkinterface or adapter 123. When used in a WAN networking environment, themobile device movement detection system 101 may include a modem 127 orother means for establishing communications over the WAN 129, such asnetwork 131 (e.g., the Internet). When used in a wirelesstelecommunications network 133, the mobile device movement detectionsystem 101 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 141 (e.g., mobile phones, short-range vehiclecommunication systems, vehicle telematics devices) via one or morenetwork devices 135 (e.g., base transceiver stations) in the wirelessnetwork 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and mobiledevice location and configuration system components described herein maybe configured to communicate using any of these network protocols ortechnologies.

Also illustrated in FIG. 1 is a security and integration layer 160,through which communications may be sent and managed between the mobiledevice movement detection system 101 (e.g., a user's personal mobiledevice, a vehicle-based system, external server, etc.) and the remotedevices (141 and 151) and remote networks (125, 129, and 133). Thesecurity and integration layer 160 may comprise one or more separatecomputing devices, such as web servers, authentication servers, and/orvarious networking components (e.g., firewalls, routers, gateways, loadbalancers, etc.), having some or all of the elements described abovewith respect to the mobile device movement detection system 101. As anexample, a security and integration layer 160 of a mobile computingdevice, vehicle-based device, or a server operated by an insuranceprovider, financial institution, governmental entity, or otherorganization, may comprise a set of web application servers configuredto use secure protocols and to insulate the mobile device movementdetection system 101 from external devices 141 and 151. In some cases,the security and integration layer 160 may correspond to a set ofdedicated hardware and/or software operating at the same physicallocation and under the control of same entities as the mobile devicemovement detection system 101. For example, layer 160 may correspond toone or more dedicated web servers and network hardware in anorganizational datacenter or in a cloud infrastructure supporting acloud-based mobile device location and configuration system. In otherexamples, the security and integration layer 160 may correspond toseparate hardware and software components which may be operated at aseparate physical location and/or by a separate entity.

As discussed below, the data transferred to and from various devices indistracted driving analysis system 100 may include secure and sensitivedata, such as driving data, driving locations, vehicle data, andconfidential individual data such as insurance data associated withvehicle occupants. In at least some examples, transmission of the datamay be performed based on one or more user permissions provided.Therefore, it may be desirable to protect transmissions of such data byusing secure network protocols and encryption, and also to protect theintegrity of the data when stored in a database or other storage in amobile device, analysis server, or other computing devices in thedistracted driving analysis system 100, by using the security andintegration layer 160 to authenticate users and restrict access tounknown or unauthorized users. In various implementations, security andintegration layer 160 may provide, for example, a file-based integrationscheme or a service-based integration scheme for transmitting databetween the various devices in the distracted driving analysis system100. Data may be transmitted through the security and integration layer160, using various network communication protocols. Secure datatransmission protocols and/or encryption may be used in file transfersto protect to integrity of the driving data, for example, File TransferProtocol (FTP), Secure File Transfer Protocol (SFTP), and/or Pretty GoodPrivacy (PGP) encryption.

In other examples, one or more web services may be implemented withinthe mobile device movement detection system 101 in the distracteddriving analysis system 100 and/or the security and integration layer160. The web services may be accessed by authorized external devices andusers to support input, extraction, and manipulation of the data (e.g.,driving data, location data, confidential personal data, etc.) betweenthe mobile device movement detection system 101 in the distracteddriving analysis system 100. Web services built to support thedistracted driving analysis system 100 may be cross-domain and/orcross-platform, and may be built for enterprise use. Such web servicesmay be developed in accordance with various web service standards, suchas the Web Service Interoperability (WS-I) guidelines. In some examples,a movement data and/or driving data web service may be implemented inthe security and integration layer 160 using the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between servers (e.g., mobile device movement detectiondevices 101) and various clients 141 and 151 (e.g., mobile devices, dataanalysis servers, etc.). SSL or TLS may use HTTP or HTTPS to provideauthentication and confidentiality.

In other examples, such web services may be implemented using theWS-Security standard, which provides for secure SOAP messages using XMLencryption. In still other examples, the security and integration layer160 may include specialized hardware for providing secure web services.For example, secure network appliances in the security and integrationlayer 160 may include built-in features such as hardware-accelerated SSLand HTTPS, WS-Security, and firewalls. Such specialized hardware may beinstalled and configured in the security and integration layer 160 infront of the web servers, so that any external devices may communicatedirectly with the specialized hardware.

Although not shown in FIG. 1, various elements within memory 115 orother components in the distracted driving analysis system 100, mayinclude one or more caches, for example, CPU caches used by theprocessing unit 103, page caches used by the operating system 117, diskcaches of a hard drive, and/or database caches used to cache contentfrom database 121. For embodiments including a CPU cache, the CPU cachemay be used by one or more processors in the processing unit 103 toreduce memory latency and access time. In such examples, a processor 103may retrieve data from or write data to the CPU cache rather thanreading/writing to memory 115, which may improve the speed of theseoperations. In some examples, a database cache may be created in whichcertain data from a database 121 (e.g., a driving database, a vehicledatabase, insurance customer database, etc.) is cached in a separatesmaller database on an application server separate from the databaseserver. For instance, in a multi-tiered application, a database cache onan application server can reduce data retrieval and data manipulationtime by not needing to communicate over a network with a back-enddatabase server. These types of caches and others may be included invarious embodiments, and may provide potential advantages in certainimplementations of retrieving and analyzing sensor data, such as fasterresponse times and less dependence on network conditions whentransmitting/receiving sensor data, vehicle data, occupant data, etc.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computer devices and systemcomponents described herein may be configured to communicate using anyof these network protocols or technologies.

Additionally, one or more application programs 119 may be used by themobile device movement detection system 101 within the distracteddriving analysis system 100 (e.g., mobile device movement detectionsoftware applications, and the like), including computer executableinstructions for receiving and storing data from sensors of mobiledevices, and/or vehicle-based systems, analyzing the sensor data todetermine whether there is a mobile device movement event, calculating arisk score based on aggregated sensor data, providing feedback regardingthe risk score, and/or performing other related functions as describedherein.

FIG. 2 is an illustration of an example implementation of a distracteddriving analysis system 200. The distracted driving analysis system 200may be similar to and/or may include some or all of the components ofthe distracted driving analysis system 100 in FIG. 1. The system 200, inthis example, includes a mobile device movement detection system 202.The mobile device movement detection system 202, described in furtherdetail below, detects movement events relating to a mobile deviceassociated with a vehicle. The mobile device movement detection system202 may be similar to and/or may include some or all of the componentsof the mobile device movement detection system 101 in FIG. 1. In someexamples, the mobile device movement detection system 202 may detect amobile device movement event based on sensor data received from one ormore mobile devices associated with the vehicle.

The example distracted driving analysis system 200 may contain some orall of the hardware/software components as the distracted drivinganalysis system 100 depicted in FIG. 1. The mobile device movementdetection system 202 is a special-purpose computing device that isconfigured to receive sensor data from a mobile device 212 locatedwithin a vehicle 204. The mobile device movement detection system 202may initiate communication with, retrieve data from, or receive sensordata (e.g., signals) from one or more sensors within a mobile device 212wirelessly over one or more computer networks (e.g., the Internet),where the mobile device 212 is located within a vehicle 204. The mobiledevice movement detection system 202 may also be configured to receivedriving data from a vehicle 204 wirelessly via telematics device 206, orby way of separate computing systems (e.g., computer 240) over one ormore computer networks (e.g., the Internet). Further, the mobile devicemovement detection system 202 may be configured to receive drivingvehicle-related data from one or more third-party telematics systems ornon-vehicle data sources, such as external traffic databases containingtraffic data (e.g., amounts of traffic, average driving speed, trafficspeed distribution, and numbers and types of accidents, etc.) at varioustimes and locations, external weather databases containing weather data(e.g., rain, snow, sleet and hail amounts, temperatures, wind, roadconditions, visibility, etc.) at various times and locations, and otherexternal data sources containing driving hazard data (e.g., roadhazards, traffic accidents, downed trees, power outages, constructionzones, school zones, and natural disasters, etc.).

A mobile device 212 in the distracted driving analysis system 200 maybe, for example, any mobile device, such as a smartphone, tabletcomputing device, personal digital assistant (PDA), smart watch,netbook, laptop computer, and other like devices found within a vehicle204. As used herein, a mobile device 212 “within” the vehicle 204includes mobile devices that are inside of or otherwise secured to avehicle, for instance, in the cabins of a vehicle. The mobile device 212includes a set of mobile device sensors 214, which may include, forexample, an accelerometer 216, a GPS receiver 218, a gyroscope 220, amicrophone 222, a camera 224, and a magnetometer 226. The mobile devicesensors 214 may be capable of detecting and recording various conditionsat the mobile device 112 and operational parameters of the mobile device112. For example, sensors 214 may detect and store data corresponding tothe mobile device's location (e.g., GPS coordinates), speed anddirection in one or multiple axes (forward and back, left and right, andup and down for example), rate and direction of acceleration ordeceleration, specific instances of sudden acceleration, deceleration,lateral movement, and other data which may be indicative of a mobiledevice movement event. Additional sensors 214 may include audio sensors,video sensors, signal strength sensors, communication network-presencesensors, ambient light sensors, temperature/humidity sensors, and/orbarometer sensors, which may be used to, for example, listen to audiosignals indicating a door locking/unlocking, door chime, or vehicleignition, sensing light from an overhead or dashboard light, detecting atemperature or humidity change indicative of entering a vehicle, and/ordetecting a presence of a network or communication device associatedwith a vehicle (e.g., a BLUETOOTH transceiver associated with avehicle).

Software applications executing on mobile device 212 may be configuredto detect certain driving data independently using mobile device sensors214. For example, mobile device 212 may be equipped with sensors 214,such as an accelerometer 216, a GPS receiver 218, a gyroscope 220, amicrophone 222, a camera 224, and/or a magnetometer 226, and maydetermine vehicle location, speed, acceleration/deceleration, directionand other basic driving data without needing to communicate with thevehicle sensors 210, or any vehicle system. In other examples, softwareon the mobile device 212 may be configured to receive some or all of thedriving data collected by vehicle sensors 210.

Additional sensors 214 may detect and store external conditions. Forexample, audio sensors and proximity sensors 214 may detect other nearbymobile devices, traffic levels, road conditions, traffic obstructions,animals, cyclists, pedestrians, and other conditions that may factorinto a braking event data analysis.

Data collected by the mobile device sensors 214 may be stored,processed, and/or analyzed within the mobile device 212, and/or may betransmitted to one or more external devices for processing, analysis,and the like. For example, as shown in FIG. 2, sensor data collected bythe mobile device sensors 214 may be transmitted to a mobile devicemovement detection system 202. In some examples, the data collected bythe mobile device sensors 214 may be stored, processed, and/or analyzedat the vehicle 204 by an on-board computing device in the vehicle or bythe mobile device 212, and/or may be transmitted to one or more externaldevices (e.g., an insurance system 244). For example, sensor data may beexchanged (uni-directionally or bi-directionally) between vehicle 204and mobile device 212.

Data may be transmitted between the mobile device 212 and the vehicle204 via wireless networks, including those discussed above, orshort-range communication systems. Short-range communication systems aredata transmission systems configured to transmit and receive databetween nearby devices. In this example, short-range communicationsystems may be used to transmit sensor data to other nearby mobiledevices and/or vehicles, and to receive sensor data from other nearbymobile devices and/or vehicles. Short-range communication systems may beimplemented using short-range wireless protocols such as WLANcommunication protocols (e.g., IEEE 802.11), Bluetooth (e.g., IEEE802.15.1), or one or more of the Communication Access for Land Mobiles(CALM) wireless communication protocols and air interfaces. Thetransmissions between the short-range communication systems may be sentvia Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID, and/orany suitable wireless communication media, standards, and protocols. Incertain systems, short-range communication systems may includespecialized hardware installed in vehicle 204 and/or mobile device 212(e.g., transceivers, antennas, etc.), while in other examples thecommunication systems may be implemented using existing hardwarecomponents (e.g., radio and satellite equipment, navigation computers)or may be implemented by software running on the mobile device 212and/or on an on-board computing device within the vehicle 204.

The vehicle 204 may be, for example, an automobile, motorcycle, scooter,bus, recreational vehicle, boat, bicycle, or other vehicle in which amobile device may be located. The vehicle 204 may include one or moresensors 210, which are capable of detecting and recording variousconditions at the vehicle and operating parameters of the vehicle. Forexample, the sensors 210 may detect, transmit, or store datacorresponding to the vehicle's location (e.g., GPS coordinates), speedand direction, rate and direction of acceleration, deceleration, and/ormay detect transmit specific instances of sudden acceleration, suddendeceleration, and swerving. The sensors 210 may also detect, transmit,or store data received from the vehicle's internal systems, such asimpact to the body of the vehicle, air bag deployment, headlights usage,brake light operation, door opening and closing, door locking andunlocking, cruise control usage, hazard lights usage, windshield wiperusage, horn usage, turn signal usage, seat belt usage, phone and radiousage within the vehicle, maintenance performed on the vehicle, andother data collected by the vehicle's computer systems. Thus, in someexamples, the mobile device movement detection system 202 may acquireinformation about the vehicle 204 directly from the vehicle 204.

Additional sensors 210 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. Additional sensors 210 may alsodetect and store data relating to compliance with traffic laws and theobservance of traffic signals and signs. Additional sensors 210 mayfurther detect and store data relating to the maintenance of the vehicle204, such as the engine status, oil level, engine coolant temperature,odometer reading, the level of fuel in the fuel tank, engine revolutionsper minute (RPMs), tire pressure, or combinations thereof.

The vehicle 204 may also include cameras or proximity sensors 210capable of recording additional conditions inside or outside of thevehicle 204. For example, internal cameras 210 may detect conditionssuch as the number of passengers and the types of passengers (e.g.,adults, children, teenagers, handicapped, etc.) in the vehicles, andpotential sources of driver distraction within the vehicle (e.g., pets,phone usage, unsecured objects in the vehicle). Sensors 210 also may beconfigured to collect data a driver's movements or the condition of adriver. For example, the vehicle 204 may include sensors 210 thatmonitor a driver's movements, such as the driver's eye position and/orhead position, etc. Additional sensors 210 may collect data regardingthe physical or mental state of the driver, such as fatigue orintoxication. The condition of the driver may be determined through themovements of the driver or through other sensors, for example, sensorsthat detect the content of alcohol in the air or blood alcohol contentof the driver, such as a breathalyzer. Further, the vehicle 204 mayinclude sensors 210 that are capable of detecting other nearby vehicles,traffic levels, road conditions, traffic obstructions, animals,cyclists, pedestrians, and other conditions that may factor into ananalysis of vehicle telematics data. Certain vehicle sensors 210 alsomay collect information regarding the driver's route choice, whether thedriver follows a given route, and to classify the type of trip (e.g.,commute, errand, new route, etc.). A Global Positioning System (GPS),locational sensors positioned inside the vehicle 204, and/or locationalsensors or devices external to the vehicle 204 may be used determine theroute, trip type (e.g., commute, errand, new route, etc.), laneposition, and other vehicle position or location data.

The data collected by the vehicle sensors 210 may be stored or analyzedwithin the respective vehicle 204 by an on-board computing device ormobile device 212, or may be transmitted to one or more externaldevices. For example, as shown in FIG. 2, sensor data may be transmittedto a mobile device movement detection system 202, which may be acollection of special-purpose computing devices that are interconnectedand in signal communication with each other. The special-purposecomputing devices may be programmed with a particular set ofinstructions that, when executed, perform functions associated withprocessing the sensor data to detect mobile device movement events,calculating a risk score, and generating and/or providing feedback tothe mobile device or vehicle based on the calculated risk score. Assuch, a mobile device movement detection system 202 may be a separatespecial-purpose computing device or may be integrated into one or morecomponents within the vehicle 204, such as the telematics device 206, orin the internal computing systems (e.g., on-board vehicle computingdevice) of the vehicle 204. Additionally, the sensor data may betransmitted as vehicle telematics data via a telematics device 206 toone or more remote computing devices, such as a mobile device movementdetection system 202. A telematics device 206 may be a computing devicecontaining many or all of the hardware/software components as the mobiledevice movement detection system 101 depicted in FIG. 1. As discussedabove, the telematics device 206 may receive vehicle telematics datafrom vehicle sensors 210, and may transmit the data to one or moreexternal computer systems (e.g., an insurance system 244) over awireless network. Telematics devices 206 also may be configured todetect or determine additional types of data relating to real-timedriving and the condition of the vehicle 204. In certain embodiments,the telematics device 206 may contain or may be integral with one ormore of the vehicle sensors 210. The telematics device 206 may alsostore the type of the vehicle 204, for example, the make, model, trim(or sub-model), year, and/or engine specifications, as well as otherinformation such as vehicle owner or driver information, insuranceinformation, and financing information for the vehicle 204.

In the example shown in FIG. 2, the telematics device 206 may receivevehicle telematics data from vehicle sensors 210, and may transmit thedata to a mobile device movement detection system 202. However, in otherexamples, one or more of the vehicle sensors 210 may be configured totransmit data directly to a mobile device movement detection system 202without using a telematics device 206. For instance, a telematics device206 may be configured to receive and transmit data from certain vehiclesensors 210, while other sensors may be configured to directly transmitdata to a mobile device movement detection system 202 without using thetelematics device 206. Thus, telematics devices 206 may be optional incertain embodiments.

In certain embodiments, the mobile device 212 within the vehicle 204 maybe programmed with instructions to collect vehicle telematics data fromthe telematics device 206 or from the vehicle sensors 210, and then totransmit the vehicle telematics data to the mobile device movementdetection system 202 and other external computing devices. For example,the mobile device 212 may transmit the vehicle telematics data directlyto a mobile device movement detection system 202, and thus may be usedin conjunction with or instead of the telematics device 206. Moreover,the processing components of the mobile device 212 may be used tocollect and analyze sensor data and/or vehicle telematics data to detectmobile device movement events, calculate a risk score, provide feedbackto mobile device or vehicle based on the risk score, and perform otherrelated functions. Therefore, in certain embodiments, the mobile device212 may be used in conjunction with or instead of the mobile devicemovement detection unit 208.

The vehicle 204 may include a mobile device movement detection unit 208,which may be a separate computing device or may be integrated into oneor more other components within the vehicle 204, such as the telematicsdevice 206, the internal computing systems of the vehicle 204, and/orthe mobile device 212. In some examples, the mobile device 212 mayinclude a mobile device movement detection unit 230 which may be adevice separate and independent from the mobile device movementdetection unit 208 of the vehicle 204. The mobile device movementdetection units 208 and 230 may alternatively be implemented bycomputing devices separate and independent from the vehicle 204 and themobile device 212, such as one or more computer systems 240. In any ofthese examples, the mobile device movement detection units 208 and 230may contain some or all of the hardware/software components as themobile device movement detection system 101 depicted in FIG. 1.

The mobile device movement detection units 208 and 230 may beimplemented in hardware and/or software configured to receive raw sensordata from the vehicle sensors 210 and the mobile device sensors 214respectively, and/or other vehicle telematics data sources. The mobiledevice movement detection unit 208 may further be configured to receivesensor data from a telematics device 206. After receiving the sensordata and vehicle telematics data, the mobile device movement detectionunits 208 and 230 may process the sensor data and vehicle telematicsdata, and analyze the sensor data and vehicle telematics data todetermine whether a mobile device movement event occurred at aparticular time. One or more notifications including feedback may begenerated based on a calculation of a risk score to the mobile device212 or vehicle 204. For example, the mobile device movement detectionunits 208 and 230 may analyze the sensor data collected from the mobilesensors 214 and the vehicle sensors 210. The mobile device movementdetection units 208 and 230 may determine whether there is a thresholdchange in the direction of acceleration of the mobile device 212. Inexamples where there is a threshold change in the direction ofacceleration of the mobile device 212, the mobile device movementdetection units 208 and 230 may determine that a mobile device movementevent has occurred as a particular time. The mobile device movementdetection units 208 and 230 may then aggregate sensor data and vehicletelematics data associated with a window of time encompassing the mobiledevice movement event, and calculate a risk score based on theaggregated data. The mobile device movement detection units 208 and 230may then generate and provide feedback to the mobile device 212 orvehicle 204 based on the calculated risk score.

Further, in certain implementations, the functionality of the mobiledevice movement detection units 208 and 230, such as collecting andanalyzing sensor data to detect mobile device movement events,aggregating sensor data and vehicle telematics data, calculating a riskscore based on the aggregated data, and providing notifications to thedriver or vehicle based on the calculated risk score, may be performedin a mobile device movement detection system 202 rather than by theindividual vehicle 204 or mobile device 212. In such implementations,the vehicle 204 or mobile device 212 may only collect and transmitsensor data to a mobile device movement detection system 202, and thusthe mobile device movement detection units 208 and 230 may be optional.Thus, in various examples, the analyses and actions performed within themobile device movement detection units 208 and 230 may be performedentirely within the mobile device movement detection units 208 and 230,entirely within the mobile device movement detection system 202, or insome combination of the two. For instance, the mobile device movementdetection units 208 and 230 may continuously receive and analyze sensordata and determine whether the sensor data indicates a change in thedirection of acceleration/deceleration that is above a predefinedthreshold. While the changes in the direction ofacceleration/deceleration are below the predefined threshold (i.e.,there is minimal likelihood of a mobile device movement event), themobile device movement detection units 208 and 230 may continue toreceive and analyze data, such that large or repetitive amounts of dataneed not be transmitted to the mobile device movement detection system202. However, upon detecting a change in the direction ofacceleration/deceleration above the predefined threshold, the mobiledevice movement detection units 208 and 230 may transmit sensor data andvehicle telematics data associated with a window of time encompassingthe mobile device movement event to the mobile device movement detectionsystem 202, such that the mobile device movement detection system 202may aggregate the sensor data and vehicle telematics data associatedwith the window of time to calculate a risk score for that window oftime.

Additional arrangements, as well as detailed descriptions and examplesof the analyses that may be performed by the mobile device movementdetection units 208 and 230 and/or by the mobile device movementdetection system 202 are described below.

FIG. 3 shows an example implementation of a mobile device movementdetection system 202. In some example implementations, the mobile devicemovement detection system 202 is a special-purpose computing deviceprogrammed with instructions, that when executed, perform functionsassociated with collecting or receiving sensor data from mobile devicesand vehicles, processing the sensor data, determining whether a mobiledevice movement event occurred at a particular time, aggregating sensordata over a window of time encompassing the mobile device movementevent, calculating a risk score based on the aggregated sensor data, andgenerating and/or providing feedback to the mobile device or vehiclebased on the calculated risk score. In these example implementations,the units 302-312 of the mobile device movement detection system 202correspond to particular sets of instructions embodied as softwareprograms residing at the mobile device movement detection system 202. Inother example implementations, the mobile device movement detectionsystem 202 is a collection of special-purpose computing devices that areinterconnected and in signal communication with each other. In theseexamples, each unit or device 302-312 of the mobile device movementdetection system 202 respectively corresponds to a special-purposecomputing device programmed with a particular set of instructions, that,when executed, perform respective functions associated with collectingor receiving sensor data from mobile devices and vehicles, processingthe sensor data, determining whether a mobile device movement eventoccurred at a particular time, aggregating sensor data over a window oftime encompassing the mobile device movement event, calculating a riskscore based on the aggregated sensor data, and generating and/orproviding feedback to the mobile device or vehicle based on thecalculated risk score. Such special-purpose computing devices may be,for example, application servers programmed to perform the particularset of functions.

The mobile device movement detection system 202, in this example,includes various modules, units and databases that facilitate collectingor receiving sensor data, processing the sensor data, determiningwhether a mobile device movement event occurred at a particular time,aggregating sensor data over a window of time encompassing the mobiledevice movement event, calculating a risk score based on the aggregatedsensor data, and generating and/or providing feedback to the mobiledevice or vehicle based on the calculated risk score. It will beappreciated that the mobile device movement detection system 202illustrated in FIG. 3 is shown by way of example, and that otherimplementations of a mobile device movement detection system may includeadditional or alternative modules, units, devices, and/or databaseswithout departing from the scope of the claimed subject matter. In thisexample, the mobile device movement detection system 202 includes asensor data collection module 302, a sensor data processing module 304,a movement event detection module 306, a sensor data aggregation module308, a risk determination module 310, a risk feedback generation module312, and a data store 320. Each module may include hardware and/orsoftware configured to perform various functions within the mobiledevice movement detection system 202. Further, each module may be aseparate and distinct computing device or one or more modules may beintegrated into a single computing device.

The data store 320 may store information relating to the driver of thevehicle 204, information relating to the vehicle 204, and/or informationrelating to the mobile device 212. For example, the data store 320 mayinclude a driver information database 322, and a vehicle informationdatabase 324. It will be appreciated that in other examples, the datastore 320 may include additional and/or alternative databases.

The driver information database 322 may store information associatedwith drivers of the vehicles 204 (e.g., name of driver, contactinformation, one or more associated mobile devices, one or moreassociated vehicles, etc.). In some examples, the driver informationdatabase 322 may also store the driver's affiliation with one or moreinsurance providers.

The vehicle information database 324 may store information associatedwith the vehicles 204 (e.g., make, model, mileage, last maintenancedate, accident reports, etc.).

FIG. 4 is a flowchart 400 of example steps for determining whether amobile device movement event occurred at a particular time, calculatinga risk score, and providing feedback based on the risk score accordingto one or more aspects described herein. The various components of themobile device movement detection system 202 and/or the mobile devicemovement detection unit 230 of the mobile device 212 may be used toperform these method steps.

In step 402, the sensor data collection module 302 may receiveacceleration data from the accelerometer 216 of the mobile device 212 attimes t₁ and t₂. Times t₁ and t₂ may be separated by a predefinedduration of time (e.g., one second, one millisecond, etc.), such thattime t₁ precedes time t₂ The acceleration data (signal) at times t₁ andt₂ may be represented as three-dimensional vectors having a magnitudeand a direction. In some examples, the acceleration data (signal) mayinclude a gravity component and a non-gravity component, where thegravity component represents the acceleration due to gravity and wherethe non-gravity component represents the linear acceleration due to themovement of the mobile device 212.

In step 404, the sensor data collection module 302 may receive sensordata from the one or more sensors 214 installed at, attached to, and/orremotely located relative to the mobile device 212. In some examples,the mobile device 212 may collect sensor data from the one or moresensors 214 and transmit the sensor data to the mobile device movementdetection system 202 in real-time or near real-time. As such, the mobiledevice 212 may broadcast the sensor data from the one or more sensors214, transmit the sensor data to the mobile device movement detectionunit 230 in real-time, and the mobile device movement detection unit 230may transmit the sensor data to the mobile device movement detectionsystem 202. The mobile device movement detection unit 230 may or may nottransmit the sensor data to the mobile device movement detection system202 in real-time. For instance, the mobile device movement detectionunit 230 may begin to collect sensor data from the one or more sensors214, and wait to transmit sensor data from the one or more sensors 214until the mobile device movement detection unit 230 or mobile devicemovement detection system 202 detects a mobile device movement event(e.g., in step 410). In another example, the mobile device movementdetection unit 230 may transmit sensor data to the mobile devicemovement detection system 202 in response to a request from the mobiledevice movement detection system 202 to collect and transmit sensor dataassociated with a window of time. As such, the mobile device movementdetection unit 230 advantageously limits and/or controls the number oftransmissions between the mobile device 212 and the mobile devicemovement detection system 202. Examples of sensor data collected in step404 from the sensors 214 of the mobile device 212 may includeacceleration from the accelerometer 216, location from the GPS receiver218, rotational motion from the gyroscope 220, sound from the microphone222, movement from the camera 224, and magnetization from themagnetometer 226. Further, as mentioned above, the sensor data may alsoinclude data received from sensors 210 of the vehicle 204, and/or datareceived from third-party sources (e.g., traffic data, weather data,etc.).

In certain embodiments, in addition to the sensor data obtained from thesensors 214 of the mobile device 212, the sensor data collection module302 may collect and process sensor data from the sensors 210 of thevehicle 204. The sensor data from the sensors 210 of the vehicle 204 maybe used to supplement the sensor data from the sensors 214 of the mobiledevice 212 as desired. The additional data may be beneficial inproviding increased accuracy in vehicle telematics data. For example,where signal communication with the mobile device 212 is lost, thesensor data collection module 302 may collect and process sensor datafrom the sensors 210 of the vehicle 204.

In step 406, the sensor data processing module 304 may process theacceleration data (signal) received from the accelerometer 216 of themobile device at times t₁ and t₂ In some examples, the sensor dataprocessing module 304 may apply one or more algorithms to separate theacceleration due to gravity from linear acceleration due to the movementof the mobile device 212 at times t₁ and t₂. For instance, the sensordata processing module 304 may apply a low pass filter to the originalacceleration data (signal) to isolate the acceleration due to gravity.The sensor data processing module 304 may then remove (e.g., subtract)the acceleration due to gravity from the original acceleration data(signal). Alternatively, in another example, the sensor data processingmodule 304 may apply a high pass filter to extract the linearacceleration from the original acceleration data (signal). As such, inthese examples, the processed acceleration signal represents only thelinear acceleration due to the movement of the mobile device 212.

In other examples, the sensor data processing module 304 mayadditionally or alternatively use a gravity sensor of the mobile deviceto determine the acceleration due to gravity. In these examples, thesensor data processing module 304 may then apply one or more algorithmsto remove the acceleration from gravity from the original accelerationdata (signal) to isolate the acceleration due to gravity.

In step 408, the movement event detection module 306 may determinewhether a mobile device movement event occurred at time t₂ based on theprocessed acceleration data (signal) at times t₁ and t₂.

Referring now to FIG. 5, a flowchart 500 of example method steps fordetecting a mobile device movement event is shown. The movement eventdetection module 306 of the mobile device movement detection system 202and/or of the mobile device movement detection unit 230 may be used toperform these method steps. At step 502, the movement event detectionmodule 306 may construct three-dimensional vectors representing theprocessed acceleration data (signals) at times t₁ and t₂ In someexamples, the acceleration vector at time t₁ may be represented asvector a, having an x-axis component a_(x), a y-axis component a_(y),and a z-axis component a_(z). Similarly, the acceleration vector at timet₂ may be represented as a vector b, having an x-axis component b_(x), ay-axis component b_(y), and a z-axis component b_(z).

At step 504, the movement event detection module 306 may calculate achange in angle between the vectors at times t₁ and t₂ In some examples,a formula to calculate a change of angle between vectors may be derivedfrom the formula for calculating the dot product of the vectors a and b.An example of this computation is shown below:

${a \cdot {b\begin{pmatrix}a_{x} \\a_{y} \\a_{z}\end{pmatrix}} \cdot \begin{pmatrix}b_{x} \\b_{y} \\b_{z}\end{pmatrix}} = {{{a_{x}b_{x}} + {a_{y}b_{y}} + {a_{z}b_{z}}} = {{{a}{b}\cos\mspace{11mu}\left. \alpha\Longrightarrow\cos \right.\;\alpha} = {\left. \frac{{a_{x}b_{x}} + {a_{y}b_{y}} + {a_{z}b_{z}}}{\sqrt{a_{x}^{2} + a_{y}^{2} + a_{z}^{2}}\sqrt{b_{x}^{2} + b_{y}^{2} + b_{z}^{2}}}\Longrightarrow\alpha \right. = {\cos^{- 1}\frac{{a_{x}b_{x}} + {a_{y}b_{y}} + {a_{z}b_{z}}}{\sqrt{a_{x}^{2} + a_{y}^{2} + a_{z}^{2}}\sqrt{b_{x}^{2} + b_{y}^{2} + b_{z}^{2}}}}}}}$

In step 506, the movement event detection module 306 may determinewhether the change in angle between the vectors a and b is greater thana predetermined threshold (e.g., greater than 0.2°, greater than 0.5°,etc.). Where the change in angle is above predetermined threshold instep 506, the movement event detection module 306 may determine thatthere was likely a mobile device movement event at time t₂ in step 508.Alternatively, where the change in angle is not above the predeterminedthreshold in step 506, the movement event detection module 306 maydetermine that there was likely no mobile device movement event at timet₂ in step 510.

Referring back to FIG. 4, where the movement event detection module 306determines that there was likely no mobile device movement event at timet₂ in step 410, the movement event detection module 306 may continuecollecting acceleration data for new times t₁ and t₂ in step 418. Assuch, method steps 402-410 may be repeated using a consecutive windowalgorithm, such that the consecutive windows are adjacent but notoverlapping. As such, in these examples, the mobile device movementdetection system 202 advantageously limits and/or controls the number oftransmissions between the mobile device 212 and the mobile devicemovement detection system 202. Further, the mobile device movementdetection system 202 advantageously limits and/or controls theaggregation of sensor data.

Alternatively, where the movement event detection module 306 determinesthat there was likely a movement device movement event at time t₂ instep 410, the sensor data aggregation module 308 may aggregate thesensor data collected in step 404 for a window of time encompassing themobile device movement event (e.g., encompassing the time t₂). As such,the sensor data aggregation module 308 may aggregate sensor data duringa window of time starting at a first predetermined duration before thetime t₂ and ending at a second predetermined duration after the time t₂.For instance, the sensor data aggregation module 308 may aggregatesensor data from time t₂−10 seconds to t₂+10 seconds in step 412, asshown in FIG. 6. As such, the sensor data aggregation module 308 maygather sensor data for a window of time of 21 seconds. It will beappreciated that the window of time used by the sensor data aggregationmodule 308 may be configured to use varying windows of time, such that awindow of time may be greater or lesser than 21 seconds. For example,the sensor data aggregation module 308 may be configured such that thefirst predetermined duration is longer, shorter, or the same as thesecond predetermined duration.

In step 414, the risk determination module 310 may calculate a riskscore based on the aggregated sensor data. In some examples, the riskscore may be based on a plurality of factors, including the speed of thevehicle (e.g., the minimum/maximum speed during the window of time, theaverage speed during the window of time, etc.), road type (e.g., citystreet, highway, etc.), weather, time of day, known or unknown route,and phone type (e.g., make and model of mobile device 212). Otherfactors may be tied to the acceleration data collected from the mobiledevice 212, such as the intensity of the phone movement as determined bythe magnitude of the acceleration vector at time t₂. In some examples,the risk determination module 310 may apply factors based on historicaldeterminations by the movement event detection module 306, such as thefrequency of mobile device movement events (e.g., average number ofmobile device movement events per trip, average number of mobile deviceevents per a predetermined duration of time, etc.).

In some examples, risk determination module 310 may assign a score foreach factor. For instance, the risk determination module 310 may beconfigured such that a higher score is assigned to a maximum speed ofthe vehicle above a predetermined value during the window of time,whereas a lower score is assigned to a maximum speed of the vehicleabove the predetermined value during the window of time. In a furtherexample, the risk determination module 310 may be configured such that ahigher score is assigned to driving in rainy or snow weather conditions,whereas a lower score is assigned to driving in dry weather conditions.It will be appreciated that the risk determination module 310 may besimilarly configured for other factors utilized to calculate a riskscore.

Once each factor is assigned a score, the risk determination module 310may apply a risk calculation equation to determine the risk score. Anexample risk calculation equation may be:risk score=factor[1].score+factor[2].score+ . . . +factor[n].scorewhere factor[1].score . . . factor[n].score are the respective scoresassigned to each factor. In some examples, the scores assigned to eachfactor may be weighted by the risk calculation equation. An exampleweighted risk calculation equation may be:risk score=(factor[1].score×weight[1])+(factor[2].score×weight[2])+ . .. +(factor[n].score×weight[n])where weight[1] . . . weight[n] are the weights respectively associatedwith factor[1] . . . factor[n]

It will be appreciated that additional or alternative mathematicaloperations may be selectively employed to aggregate the scores for eachfactor. It will also be appreciated that the risk determination module310 may be configured to apply one or more risk calculation equationsthat respectively use different factors with different assigned scoresand/or weights. For example, the risk determination module 310 may beconfigured to use one risk calculation equation for a driver associatedwith a first company, and a second risk calculation equation for adriver associated with a second insurance company.

In step 416, the risk feedback generation module 312 may providefeedback to the mobile device 212 or vehicle 204 based on the calculatedrisk score. The risk feedback generation module 312 may recommendproviding feedback where the calculated risk score is above apredetermined threshold, and/or where particular factors are present.For instance, the risk feedback generation module 312 may generate anotification or warning to advise the driver to stop interaction withthe mobile device 212 during weather conditions with low visibility. Inanother example, the risk feedback generation module 312 may generate anotification or warning to advise the driver to stop interaction withthe mobile device 212 while traveling at a speed of above 50 mph.

In other examples, the risk feedback generation module 312 may generatea notification or warning to advise the driver of the most significantfactor or factors contributing to a calculated risk score above apredetermined threshold. For example, where the calculated risk score isabove the threshold primarily because of the vehicle's speed during thewindow of time, the risk feedback generation module 312 may generate anotification or warning to decrease the vehicle's speed in order toimprove the calculated risk score.

The notification or warning may be, for example, a combination of audio,text, graphics, or other gestures (e.g., vibrations). In some examples,the notification or warning may be communicated to a driver of a vehicle204 via a dashboard installed or attached to the vehicle. In otherexamples, the notification or warning may be communicated to the driverof the vehicle via the mobile device 212 or a wearable device. Further,the notification may serve as a disruptive alarm to the driver of thevehicle, or a passive notification. For example, where the mobile devicemovement event occurs during more dangerous driving conditions (e.g.,high speed of the vehicle 204, rainy or snowy weather conditions, lowvisibility, peak traffic hours, etc.), or if mobile device movementevents are frequent (e.g., above a threshold amount of mobile devicemovement events detected during a trip), the risk feedback generationmodule 312 may issue an alarm. Alternatively, in these cases, the riskfeedback generation module 312 may disable the user interface of themobile device 212 to prevent further interaction with the mobile device212. Conversely, where the mobile device movement event occurs duringsafer driving conditions (e.g., non-peak traffic, high visibility, belowaverage speed of the vehicle, etc.), the risk feedback generation module312 may issue a more passive notification. For example, the riskfeedback generation module 312 may warn the driver via an audio orgraphical message on the mobile device 212, and/or through a vibrationof a vehicle component in contact with the driver (e.g., the steeringwheel, one or more pedals, etc.).

Once the risk feedback determination module 312 has provided thefeedback to the mobile device 212 or vehicle 204, the mobile devicemovement detection system 202 may continue collecting acceleration datafor new times for new times t₁ and t₂, where the new time t₁ is afterthe previous time t₂+10 seconds. As such, method steps 402-410 may berepeated using a consecutive window algorithm, such that the consecutivewindows are adjacent but not overlapping.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

What is claimed is:
 1. A mobile device movement detection systemcomprising: at least one processor; and memory storing computer-readableinstructions, that when executed by the at least one processor, causethe system to: collect, by a sensor data collection device of thesystem, acceleration data from an accelerometer associated with a mobiledevice within a vehicle at a first time and at a second time; collect,by the sensor data collection device, sensor data from sensorsassociated with the mobile device, wherein the sensors comprise theaccelerometer, a GPS receiver, and a gyroscope; process, by a sensordata processing device, the acceleration data to remove a gravitycomponent of the acceleration data, and generate a processedacceleration data at the first time and at the second time; determine,by a movement event detection device, that a mobile device movementevent has occurred at the second time based, at least in part, onthree-dimensional vectors representing the processed acceleration dataat the first time and at the second time; aggregate, by a sensor dataaggregation device, the sensor data from the sensors associated with themobile device for a window of time starting a first predeterminedduration before the second time and ending a second predeterminedduration after the second time, and generate an aggregated sensor data;determine, by a risk determination device, a risk score based, at leastin part, on the aggregated sensor data for the window of time; andgenerate, by a risk feedback generation device, a notification to themobile device based, at least in part, on the risk score.
 2. The systemof claim 1 wherein determining that a mobile device movement event hasoccurred at the second time includes: calculating, by the movement eventdetection device, a difference in angle between the three-dimensionalvectors representing the processed acceleration data at the first timeand the second time; and determining, by the movement event detectiondevice, that the difference in angle is greater than a predeterminedthreshold angle.
 3. The system of claim 1 wherein processing theacceleration data to remove the gravity component of the accelerationdata includes: applying, by the sensor data processing device, a lowpass filter to the acceleration data at the first time to isolate anacceleration due to gravity at the first time; removing, by the sensordata processing device, the acceleration due to gravity at the firsttime from the acceleration data at the first time; applying, by thesensor data processing device, the low pass filter to the accelerationdata at the second time to isolate an acceleration due to gravity at thesecond time; and removing, by the sensor data processing device, theacceleration due to gravity at the second time from the accelerationdata at the second time.
 4. The system of claim 1, further includinginstructions that, when executed by the at least one processor, causethe system to: collect, by the sensor data collection device,supplemental sensor data from sensors associated with the vehicle duringthe window of time, and aggregate, by the sensor data aggregationdevice, to generate supplemental aggregated sensor data; and determine,by the risk determination device, a risk score based, at least in part,on the aggregated sensor data and the supplemental aggregated sensordata.
 5. The system of claim 1, wherein the risk score is based on aplurality of scores assigned to a plurality of factors, wherein theplurality of factors comprise an average speed of the vehicle during thewindow of time, a weather condition during the window of time, a time ofday during the window of time, a phone type of the mobile device, and amagnitude of the processed acceleration data at the second time.
 6. Thesystem of claim 5, further including instructions that, when executed bythe at least one processor, cause the system to: apply, by the riskdetermination device, a risk calculation equation aggregating theplurality of scores assigned to the plurality of factors to calculatethe risk score.
 7. The system of claim 1, further including instructionsthat, when executed by the at least one processor, cause the system to:prevent, by the risk feedback generation device, interaction with a userinterface of the mobile device responsive to the risk score.
 8. A methodcomprising: collecting, by a sensor data collection device of a mobiledevice movement detection system, acceleration data from anaccelerometer associated with a mobile device within a vehicle at afirst time and at a second time; collecting, by the sensor datacollection device, sensor data from sensors associated with the mobiledevice, wherein the sensors comprise the accelerometer, a GPS receiver,and a gyroscope; processing, by a sensor data processing device, theacceleration data to remove a gravity component of the accelerationdata, and generating a processed acceleration data at the first time andat the second time; determining, by a movement event detection device,that a mobile device movement event has occurred at the second timebased, at least in part, on three-dimensional vectors representing theprocessed acceleration data at the first time and at the second time;aggregating, by a sensor data aggregation device, the sensor data fromthe sensors associated with the mobile device for a window of timestarting at a first predetermined duration before the second time andending a second predetermined duration after the second time, andgenerating an aggregated sensor data; determining, by a riskdetermination device, a risk score based, at least in part, on theaggregated sensor data for the window of time; and generating, by a riskfeedback generation device, a notification to the mobile device based,at least in part, on the risk score.
 9. The method of claim 8 whereindetermining that a mobile device movement event has occurred at thesecond time includes: calculating, by the movement event detectiondevice, a difference in angle between the three-dimensional vectorsrepresenting the processed acceleration data at the first time and thesecond time; and determining, by the movement event detection device,that the difference in angle is greater than a predetermined thresholdangle.
 10. The method of claim 8 wherein processing the accelerationdata to remove the gravity component of the acceleration data includes:applying, by the sensor data processing device, a low pass filter to theacceleration data at the first time to isolate an acceleration due togravity at the first time; removing, by the sensor data processingdevice, the acceleration due to gravity at the first time from theacceleration data at the first time; applying, by the sensor dataprocessing device, the low pass filter to the acceleration data at thesecond time to isolate an acceleration due to gravity at the secondtime; and removing, by the sensor data processing device, theacceleration due to gravity at the second time from the accelerationdata at the second time.
 11. The method of claim 8, further comprising:collecting, by the sensor data collection device, supplemental sensordata from sensors associated with the vehicle during the window of time,and aggregate, by the sensor data aggregation device, to generate ansupplemental aggregated sensor data; and determining, by the riskdetermination device, a risk score based, at least in part, on theaggregated sensor data and the supplemental aggregated sensor data. 12.The method of claim 8, wherein the risk score is based on a plurality ofscores assigned to a plurality of factors, wherein the plurality offactors comprise an average speed of the vehicle during the window oftime, a weather condition during the window of time, a time of dayduring the window of time, a phone type of the mobile device, and amagnitude of the processed acceleration data at the second time.
 13. Themethod of claim 12, further comprising: applying, by the riskdetermination device, a risk calculation equation aggregating theplurality of scores assigned to the plurality of factors to calculatethe risk score.
 14. The method of claim 8, further comprising:preventing, by the risk feedback generation device, interaction with auser interface of the mobile device responsive to the risk score.
 15. Acomputer-assisted method of detecting mobile device movement eventscomprising: collecting, by a sensor data collection device of a mobiledevice movement detection system, acceleration data from anaccelerometer associated with a mobile device within a vehicle at afirst time and at a second time; collecting, by the sensor datacollection device, sensor data from sensors associated with the mobiledevice, wherein the sensors comprise the accelerometer, a GPS receiver,and a gyroscope; processing, by a sensor data processing device, theacceleration data to remove a gravity component of the accelerationdata, and generating a processed acceleration data at the first time andat the second time; determining, by a movement event detection device,that a mobile device movement event has occurred at the second timebased, at least in part, on three-dimensional vectors representing theprocessed acceleration data at the first time and at the second time;aggregating, by a sensor data aggregation device, the sensor data fromthe sensors associated with the mobile device for a window of timestarting at a first predetermined duration before the second time andending a second predetermined duration after the second time, andgenerating an aggregated sensor data; determining, by a riskdetermination device, a risk score based, at least in part, on theaggregated sensor data for the window of time; and generating, by a riskfeedback generation device, a notification to the mobile device based,at least in part, on the risk score.
 16. The computer-assisted method ofclaim 15 wherein determining that a mobile device movement event hasoccurred at the second time includes: calculating, by the movement eventdetection device, a difference in angle between the three-dimensionalvectors representing the processed acceleration data at the first timeand the second time; and determining, by the movement event detectiondevice, that the difference in angle in greater than a predeterminedthreshold angle.
 17. The computer-assisted method of claim 15 whereinprocessing the acceleration data to remove the gravity component of theacceleration data includes: applying, by the sensor data processingdevice, a low pass filter to the acceleration data at the first time toisolate an acceleration due to gravity at the first time; removing, bythe sensor data processing device, the acceleration due to gravity atthe first time from the acceleration data at the first time; applying,by the sensor data processing device, the low pass filter to theacceleration data at the second time to isolate an acceleration due togravity at the second time; and removing, by the sensor data processingdevice, the acceleration due to gravity at the second time from theacceleration data at the second time.
 18. The computer-assisted methodof claim 15, wherein the risk score is based on a plurality of scoresassigned to a plurality of factors, wherein the plurality of factorscomprise an average speed of the vehicle during the window of time, aweather condition during the window of time, a time of day during thewindow of time, a phone type of the mobile device, and a magnitude ofthe processed acceleration data at the second time.
 19. Thecomputer-assisted method of claim 18 further comprising: applying, bythe risk determination device, a risk calculation equation aggregatingthe plurality of scores assigned to the plurality of factors tocalculate the risk score.
 20. The computer-assisted method of claim 15further comprising: preventing, by the risk feedback generation device,interaction with a user interface of the mobile device responsive to therisk score.